Breast Cancer Classification Using Pre-trained CNNs with Explainable AI for Enhanced Decision Support

Prevention of any disease in the early stage is a blessing for any person. If it is related to cancer, then getting rid of it from the beginning itself is a blessing. Hence, early detection of this kind of disease is very much important for ensuring the effective treatment of breast cancer of the patient. The primary goal of this paper is to develop a novel deep learning approach to categorize breast cancer and make the decision-making process of the trained models more trustable and transparent. For this classification purpose, we collect a breast cancer related dataset. As the sourced dataset is unbalanced, we augment the data to improve the performance of the models. We perform the necessary preprocessing to refine the dataset to feed the model. For classification purposes, we use 4 pre-trained CNN models such as EfficientNetV2S, InceptionResNetV2, EfficientNetV2M, and XceptionNet. To enrich the interpretation of the trained models, we use explainable AI techniques including Faster ScoreCAM and LIME. Deploying AI-driven diagnostic technologies into clinical workflows requires greater transparency, and for this reason, we need to utilize Explainable AI to ensure the interpretability of trained models. Among all implemented models, EfficientNetV2S achieved the highest accuracy with 91.02%. However, the present results provide a beneficial observation to the performance and explainability of deep learning models in breast cancer classification. Source Code: https://github.com/ZobayerAkib/Breast-Cancer-Classification-ECCE2025-IEEE2025